MODERN IRRIGATION SYSTEMS

ABSTRACT

This paper proposes a new irrigation system using fuzzy logic technique by mapping the knowledge and experience of a traditional farmer. Fuzzy logic control, which is similar to the human way of thinking, has emerged as the most active tool in automatic control. The purpose of fuzzy logic controller is to automatically achieve and maintain some desired state of a system and process by monitoring system variables as well as taking appropriate control action.

The aim of this work is to develop an intelligent control using fuzzy logic approach for irrigation of agricultural field, which simulates or emulates the human being’s intelligence. The status of any agricultural field, in terms of evapotranspiration and error may be assumed as input parameters and the decision is made to determine the amount of water required for the area to be irrigated, well in advance. This leads to use effective utilization of various resources like water and electricity and hence becomes a cost effective system for the expected yield.

INTRODUCTION

In the past few years, there has been an increasing interest in the application of the fuzzy set theory to many control problems. For many complex control systems, the construction of an ordinary model is difficult due to nonlinear and time varying nature of the system. Fuzzy Control has been applied in traditional control systems, which yields promising results, It is applied for the processes, which yields promising results, it is applied for the processes, which are too complex to be analyzed by conventional techniques or where the available information is uncertain. In fact, fuzzy logic controller (FLC) is easier to prototype, simple to describe and verify, can be maintained and also extended with grater accuracy in less time. These advantages make fuzzy logic technology to be used for irrigation system also.

NEED FOR MODERN IRRIGATION SYSTEM

Water and electricity should be optimally utilized in an agricultural like India. The development in the filed of science and technology should be appropriately used in the field of agriculture for better yields. Irrigation has traditionally resulted in excessive labour and nonuniformity in water application across the filed. Hence, an automatic irrigation system is required to reduce the labour cost and to give uniformity in water application across the field.

PHYSIOLOGICAL PROCESSING

In the irrigation system, plant take-varying quantities of water at different stages of plant growth. Unless adequate and timely supply of water is assured, the physiological activities taking place within the plant are bound to be adversely affected, thereby resulting in reduced yield of crop. The amount of water to be irrigated in an irrigation schedule depends upon the evapotranspiration(ET)  from adjacent soil and from plant leaves at that specified time. The rate of ET of a given crop is influenced by its growth stages, environmental conditions and crop management. The consumptive use or evapotranspiration for a given crop at a given place may vary through out the day, through out the month and through out the crop period. Values of daily consumptive use or monthly consumptive use are determined for a given crop and at a given place. It also varies from crop to crop. There are several elimatological factors, which will influence and decide the rate of evaporation. Some of the important factors of elimate influencing the evaporation are radiation, temperature, humidity and wind speed. In this work, the input variables chosen for the system are evapotranspiration and rate of change of evapotranspiration called as error and the output variable is water amount a shot

STRUCTURE OF FUZZY CONTROLLER Here, the basic internal structure of a fuzzy logic controller is presented. The FLC allows one to use a control strategy expressed in the form of linguistic rules for the definition of an automatic control strategy. A typical fuzzy logic controller can be decomposed into four basic components as shown in Fig.2.   FUZZIFICATION UNIT It converts a crisp process state into a fuzzy state so that it is compatible with the fuzzy set representation of the process required by the inference unit. KNOWLEDGE BASE The Knowledge base consists of two components. A rule base, which describes the behaviour of control surfaces, which involves writing the rules that  tie the input values to the output model properties. Rule formation can be framed by discussing with the experts. A database contains the definition of the fuzzy sets representing the linguistic terms used in the rules. The knowledge base is generally represented by a fuzzy associative memory. INFERENCE UNIT This unit is the core of the fuzzy controller. It generates fuzzy control actions applying the rules in the knowledge base to the current process state. It determines the degree to which each measured valued is a member of a given labeled group. A given measurement can be classified simultaneously as belonging to several linguistic groups. The degree of fulfillment (DOF) of each rule is determined by applying the rules of Boolean algebra to each linguistic group that is part of the rule. This is done for all the rules in the system. Finally the net control action is determined by weighting action associated with each rule by degree of fulfillment.  DEFUZZIFICATION UNIT It converts the fuzzy control action generated by the inference unit into a crisp value that can be used to drive the actuators. The defuzzification methods such as centroid method, center of maxima method have been predominant on fuzzy control. Perhaps the most frequently used defuzzification method is the centroid method. DESIGN PROCEDURE –FLC FOR IRRIGATION CONTROL The heart of the FLC is to form the knowledge base that can obtained form human experts is that field. In designing FLC, the following five steps are to be followed. Step 1 : Identification and Declaration of  Inputs and Output This is the basic step in which the inputs and output are identified. In the controller design for irrigation control, the inputs are evapotranspiration and error and the output is water amount. The process of declaring the values of inputs and output called universe of discourse is shown in table 1. TABLE 1. Universe of discourse Name Input/Output Min value % Max value % Evapotranspiration Input 0 100 Rate of change of Evapotranspiration Input -50 +50 Water Amount Output 0 100 Step 2 : Identification of Control Surfaces In this step, the linguistic variables are identified and membership values for each linguistic variable are calculated. In this FLC, five Linguistic variables for evapotranspiration, five Linguistic variables for error and nine linguistic variables for water output are used. They are very Low(VL), Low(L), Medium(M), High(H) and Very High(VH) for evapotranspiration: More Negative(MN),Negative(N), zero(Z), Positive(P) and More Positive(MP) for error; Drastic Low (DL), Very Low(VL), Low(L),Medium Low(ML),Medium(M), Medium High(MH), High(H), Very High(VH),Drastic High(DH) for water output. The input and output variables are represented by fuzzy membership functions as shown in Fig 3a, Fig 3b and Fig 3c.   Step 3: Behaviour of Control Surfaces Fuzzy rules are constructed in specify action for different conditions, that is the control rules the associate the fuzzy output to fuzzy inputs are derived from general knowledge of system behaviour. In this method, the rules are extracted form numerical data and then combined with linguistic information collected for experts. The rule bas for the said application is shown in Table 2. The weightage take for rules involving zero error is reduced to 0.25 for facilitating over correcting problems.   TABLE 2 RULE BASE MATRIX ERROR EVAPO TRANSPIRATION VL L M H VH MN N Z P MP DL VL L ML M VL L ML M MH L ML M MH H ML M MH H VH M MH H VH DH STEP 4 : DECISION MAKING LOGIC OF INFERENCE LOGIC It infers a system of rules through the fuzzy operator. In inference mechanism PRODUCT implication is superior to MIN implication-minimum clipping. Further a SUM combiner is better that MAX combiner for aggregation. In this work, SUM PRODUCT criteria are used to determine the outcome of rules. STEP 5: DEFUZZIFICATION                         For any given crisp input value, there may be fuzzy membership in several input variables, and each will cause several fuzzy outputs cells to fire or to be activated. This brings the process of defuzzification of output to crisp value. Centriod weightage method is used for defuzzification. RESULTS AND DISCUSSION             This work has been carried out using MATLAB simulation tool, The developed software for the proposed work was tested under different input condition and provided good results in terms of accuracy and has a wide scope of being established in near future.  By applying the fuzzy logic system, the results which were already observed (referred from IETE Technical Review) which shows that this method requires less amount of water for the same yield when compared to the method followed by the traditional farmer. The results tend to move smoothly across the control surfaces. Thus the result shown above ensures the effectiveness and accuracy of our proposed system. CONCLUSION             The work presented here brings out the potential advantages of applying FLC technique for Irrigation System. The simulation result provides an exact idea for water output for the prescribed agricultural field. Thus we conclude that, by using the proposed technique, we get the following advantages ·        Increasing Irrigation Efficiency ·        Reducing the Labour cost ·        Saving water and electricity